Centre for Computational Finance and Economic Agents
and Economics Department
Presentation
CCFEA London Stock Exchange SETS Simulator and Exercises
on How to Implement Automated VWAP and Pairs Trading
Strategies
CCFEA/i4MT Summer School Program 2008 (1-19 September 2008 )
High Frequency Finance (HFF) and Electronic Trader Training (ETT)
Professor Sheri Markose
Director CCFEA
University of Essex
Assisted by CCFEA students: Azeem Malik, Simone Giansante, Nivesh Pawar,
Shaimma Masry and Pankaj Shah
ROAD MAP OF TUTORIAL
1. Introduction
HISTORY: Challenges from Electronic Trading
New interdisciplinary area referred to as computational
market microstructure at forefront with electronic
financial markets and automated trading.
2.The CCFEA SETS Multi agent simulator Project
3. Objective pursued here to test bed algo strategies .a
VWAP and advances on VWAP
Pairs Trading
INTRODUCTION:
Potted History of E-Fin Markets
• Pre www era markets were a given from venerable forms lost in the
mystery of time
• Continuous double auction trading facilitated primarily by a market
maker or specialist system has had a long tradition
• Post www era with its electronic financial markets and automated
trading : E- markets can be designed
• Rapid decentralized and global access to e-trading platforms
• Challenges to floor-based centralized exchanges and restrictive
practices of established intermediaries
• E- online order submission began in the mid 1990s
• Early 1990’s, NYSE and LSE hybridize floor-based market maker
based continuous double auctions by replacing open outcry by an
automated screen based quotation system and execution of best
price. LSE SEAQ
Recent Developments
•
•
•
•
1/3 of all US stock trades in 2006 were driven by automatic programs
and by 2010 claimed to reach 50%. In 2006, at the London Stock
Exchange over 40% of all orders were entered by so called algo
traders and 60% is predicted for 2007. Futures and options markets
amenable to e-trading and both foreign exchange and bond markets
are moving toward this.[1]
Practitioners expending very large budgets on IT based trading
systems in order to cope with the optimal limit and market order
scheduling in a very fast and complex environment of the electronic
limit order system
Algo trading in stat-arb and VWAP strategies
Competitive co-evolution : Trading algorithms called Guerrillas ,
Snipers and Sniffers
•
[1] The electronic derivatives markets include Eurex, Globex, Matif
while those for fixed income securities are eSpeed, Euro MTS,
BonkLink and BontNet.
1.2 The new trend of computational market microstructure
DEBACLE OF NO TRADE RESULTS OF TRADITIONAL FINANCE
WITH GROWING TRADING VOLUMES
●Assumption :Fully optimizing traders necessary for efficient markets
and the postulation of the possibility of a homogenous rational
expectations HRE not tenable.
●The impossibility of a HRE in financial markets where profits arise from
being contrarian : Arthur (1991,1994) highly influential vignette of the
El Farol/Minority game
Self-referential and contrarian decision problems typical of financial
markets: DEFY a unique, homogenous and effective procedure for
finding solutions and hence deduction in forecast rules to determine
the winning strategy, Markose (200
●Endogenous heterogeneity in expectations and strategies among
initially identical agents
Red Queen type arms race in technology that is already evident in the IT
innovations of trading in E- markets
HIA and Multi-Agent Models
●New paradigm of heterogeneous interacting agent models
(Reviewed in Markose, Arifovic and Sunder (2007))
●Zero intelligence traders Gode and Sunder (1993). Fully
optimizing traders not necessary for efficient exploitation
of gains from trade; the onus on robust trading rules and
institutional design.
●Markose et al (2007) and Markose and Sunder (2007 c)
“model verite ” in artificial models : represent real time
data with no simplifying assumptions. The historical
simulator of the actual SETS1 data and the agent
abased replica of the same fall into this category of
models.
1.3 NEW HIGH FREQUENCY FINANCIAL ECONOMETRICS FOR
IRREGULARLY ARRIVING MARKET EVENTS WITH
AUTOCORRELATION/CLUSTERING
•
•
New trend with Biasis et. al. (1995) investigate the ELOB system as is.
New high frequency financial econometrics have highlighted
statistical properties of market events in at least three different ways
Engle and Russell (1994) ACD models : the clustering in trade duration
(time between trades), with short trade durations implying fast
markets.
• Dufour and Engle (2000) they find that as time duration between trades
decrease, the price impact of trades decrease and spreads decrease.
• Extensions to multivariate models with ACI models (see, Hall and
Hautsch, 2004). The ACD/ACI models point to a fundamental
persistence or the clustering first observed in trade durations can lead
to predictability and the design of trading strategies.
• ACI models can tackle multivariate analysis; Hall and Hautsch (07)
develops 6 dimensional model; Vasco Leemans (2007) analyses 12
dimensional model
 Second class of models Econometric testing of
Market Micro-Structure ( MMS)
• Based on explicit theoretical models of order placement (Sandås
(2001))or an informally stated hypothesis (see, Ellul et. al. 2007 )
then conduct econometric tests with ELOB data.
• Sandås (2001):The model based estimates are found to yield a flat
market impact function relative to the data driven estimates for the
same.
• Hasbrouck (2007) gives the most up to date discussion of the
econometric approach for the testing of hypothesis relating to
market microstructure.
●The third class of empirical studies of LOBs
Dacorogna et. al. (2001) as well as Bouchaud et. al. (2006), Farmer
et. al. (2003), Gabaix et. al. (2003) and Eisler et al. (2007) highlight
certain fat tailed properties of (limit) order price changes.
As yet no consensus of the size of the power exponent on price
changes: Farmer et. al. find power law price impact function of
0.25, Gabaix et. al report 0.5.
SELF-REFLEXIVITY IN PRICES
•
•
•
•
•
•
•
•
Arthur et. al. (1997) they make a case for heterogeneous multi-agent
models where each agent uses genetic algorithms to arrive at future
price predictions.
Why Artificial Stock Markets With Adaptive Learning Agents?
(i) Price determination is reflexive and arises from how stock market
prices are based on agents expectations of the price .Self-reflexivity :
That is, the price at t+1 is based on strategies of agents, bit (to buy or
sell) based on their respective beliefs, on the price at t+1.
The implication of this self-reflexive structure: is that there is no
there is no unique way in which agents can form expectations of
the price.
Most ASMs have agents who are heterogeneous in how they form
price forecasts.
In Arthur et. al. (1997) they make a case for heterogeneous multi-agent
models where each agent uses genetic algorithms to arrive at future
price predictions.
HAMS: TREND FOLLOWERS and
Contrarian Fundamentalists
• HAMS include popular archetypes :
• Trend followers (who accentuate the direction of historical prices)
• Fundamentalists (who effectively implement the contrarian strategy
by selling when the market price goes above a threshold and buying
when it goes below)
1.4 The demise of traditional
‘informed’/’uninformed’ trader models
• View of trading : critiqued by Rosu (2006)
Over representing the arrival of new information and on the
asymmetry of information among traders as the main drivers for
trades and their role in explaining features such as price impact and
changes in the bid-ask spreads.
• Glosten and Milgrom (1985) and Kyle (1985), so called informed and
uninformed traders interact and increased spreads arise because
market makers or liquidity providers need to protect themselves from
informed traders who aim to trade large volumes as directed by their
knowledge of the true value.
• In a dynamic price setting Easley and O’ Hara (1992) find :
Spread is an inverse function of trade durations
The absence of trades and the low volume, in this theory, imply high
probability of no information (or less asymmetry of information)
Hence risk of adverse selection faced by liquidity providers decreases
and with it the spread.
Easley and O’Hara (1992) result
contradicted
• By empirical ACD based Dufour and Engle (2001) analysis that
spreads fall under brisk trading conditions with short trade durations.
Information based theory of trades that a slow market when traders
arrive more slowly to the market has smaller spread, has seriously
damaged its empirical validity in view of serial correlation in trade
durations and the fact that more trades occur when spreads are
small.
• Laboratory experimental testing by Bloomfield et. al. (2003) of the
informed and uninformed trader hypothesis: found negative
evidence that informed agents keen to make a speedy
transformation of their informational advantage into profits will
dominate market orders. Informational advantage did not
prevent these agents from making large numbers of
limit orders as well.
• With institutional investors, large orders naturally arise from liquidity
demands and knowledge of this by other price setting parties in the
market will enable them to skew prices against the purveyors of
large orders. Following, Rosu (2006) it appears to us that fictions
such as ‘informed’ and ‘uninformed’ traders are best abandoned as
a useful frame of reference to trading in ELOBs.
2.5Models of Trader Behaviour
•
•
•
Focus on optimal order placement strategies within a game theory
framework (see, Parlour (1998), Goettler et. al. ( 2003)) have important insights
into conditions that could drive individual traders.
But they abstract from ingredients of the ELOB for purposes of analytical
tractability.
Optimal order submission strategy models: Simple one of Bertsimas and
Lo(2000), to elaborate AI based adaptive learning strategies (see, footnote 7)
with Kissel and Glantz (2003) giving a good survey of some the state of the art
order placement strategies.
• As yet no consensus as to what information is
contained in the LOB !
•
•
•
Idea first mooted in Berkowitz et. al. (1988) that the best unbiased estimate of
the price a certain quantity can be transacted at in any relevant trading period
by a randomly selected trader is given by the Volume Weighted Average Price
(VWAP) on the relevant side of the LOB book.
Order book VWAP information available ex ante to a trader
Basis of trader strategy as well as the market price impact function that is
estimated.
Part I : Artificial SETS Simulator
Market Microstructure of ELOB
2.1 The LSE SETS1 and transparent ELOB
Buy limit orders: bids
Sell limit orders: offers or asks
Best Price and Spread : (lowest ask - highest bid)
Market depth on each side of the market is the listed the
volumes
Price Priority and time priority (P, V) vector
Rank determined by uniquely different price
-1 : Competitive
0 : Best
1: 2nd best
2: ……..4+
In SETS1 market orders of a volume greater than the
volume at the best price on the LOB is allowed to walk the order
book.
That is, the remaining volume of the market order that remains after
execution at best price is converted into a limit order with time
priority only if it cannot be fully executed at the 2nd best price and so
on further down the order book.
Each of the limit prices at which the market order is executed then
assume best price status in quick succession.
• Limit orders once entered can leave the system only if they are
matched in a trade, cancellation, modification or expiry. The
maximum time a market, limit or iceberg order can sit on the order
book is 90 calendar days.
• Iceberg Orders
Objectives of CCFEA
E-LOB Project
●ASSUMPTIONS AND BUILDING OF
BENCHMARK ELOB SIMULATOR
●EXPERIMENTS : ‘SLOW’ AND ‘FAST’
MARKETS WITH EXOGENOUS MARKET
EVENTS
RESULTS FROM ELOB SIMULATOR AND
COMPARISONS WITH REAL WORLD SETS
LOB DATA FOR A TYPICAL DAY
ACD Signatures and Market Price Impact
Functions
Why Build Trading Simulators ?
Prop Trader’s view: Kerr Hatrick (Deutsche Bank)
S eas onality and S imulation
• What is a good s imulator?
– Alg orithmic trader’s definition:
•
one which, for a particular ma rket, mimics the market a s
clos ely as pos s ible, in order that reg ularities in the market might
be taken advanta ge of by ag ents us ing the s imulator
• T his means , at s ome s tag e, embedding s eas onality, and an
impact model, in s ome form
• O ther, s impler cons traints - number of trades , depth of
orderbook, typical trade s iz e – s hould be pres erved, to jus tify
the cos t es timates of the algorithm des igner, and allow
exploratory data analys is
• P enn-L ehman exchange s imulator: developed jointly
by L ehman B ros prop des k (NY ) and Univers ity of
P enns ylvania
• Utiliz es Is land order book data
• Integrates orders originating from different trading
s trategies with Is land data us ing proprietary fill model
to as s es s cos t
• F ill model – unfortunately - US centric & opaque
A Prop Trader’s Wish List for a
E-Trading Simulator contd
C os ting H F T rading S cenarios
• K ey P roblem: D ifficult to es timate cos t
s ens itivity of key algorithm parameters
• What if I changed the ag gres s ivenes s ?
• What confidence intervals can I place on the
impact of my activity with s tealth algorithm X to
be?
• At what time would it be bes t to us e algorithm x?
• What algorithm s hould I us e today?
• A s imulator cons trained by real-world
s eas onality would provide the ideal
quantitative framework to analyz e ques tions
above…
• B ut it has to embed knowledg e of local market
micros tructure in a way that is cons itent with real
11
world impact models , for derived cos ts to be
relevant
Schema of the CCFEA E-Simulator for a
SETS type market : Fully Rebuild SETS
API (III)
Format for
Clients of
e-Plat to send
in their Algo
trades. In turn
receives
Trade
Confirmation
etc
E-Platform (II)
Merge Client orders with
Real Time Historical Data
of Limit Order Book using
exact SETS Market Microstructure Rules
Provide Real Time
Screen Based Information
on Traded VWAP;
Spreads etc
Client Decision Support System (IV)
Queries Data Base (I) using SQL Commands ; Obtains
Real Time Feeds from historical Market Data in (II). This
data can be used to generate trading strategies and
evaluate strategies using the ASM AI based agents
Data Base (I)
Rebuild Limit
Order Book
SETS Data Rebuilding Order Book
SET Data
Simulator useful for testbedding
Algo Strategies
• Check robustness over different stocks
and over different market regimes
(boom/bust)
• We will consider two work horses of algo
strategies and put them through their paces:
VWAP
Pairs Trading
Algo VWAP Strategy:
Sell (buy) of X units in a given period T so that the executed
VWAP(e) is (below) than the market Traded VWAP(m)

P
j
VWAPt k
m
=
V j
j =1

V
j
j =1
where Pj and V j is the price and volume of jth trade respectively,  is the
total number of trades over the interval t0 and tk when over which the strategy is being
evaluated.
The algo- trading strategy sell (buy) for this will have the following
pseudo code :
Sell (Buy) at the bid (ask) whenever the bid (ask) is greater (less) than the
market
VWAPt j
m
 t j , t j (t 0 , t k ).
Note in the figures above for BGY, the
market VWAP is below the Bid and Ask
till about 4pm, so it is a good time to
sell as a market order but not so good
to buy. For DMG and GSK, with the Bid
and Ask below the VWAP, it gives
ample opportunities to buy, but not a
good time to sell.
Fig Price Impact of large volume
Automated : Ask < Market VWAP
Impact of 5000 Buy order Size
Explanation of large volume price
impact impact
The figures above shows how the algo VWAP strategy
to buy at ask when below market VWAP, responds to
a large order size of 5000. The first graph is the
market without the algo trade. Note, how the ask
jumps up in the second graph, followed by a sharp
rise in the bid as well below as the market finds it
difficult to absorb the large 5000 size buy order.
When failing to find enough volume at best ask, the
large buy market order, walks the book, raising the
ask immediately and hence the spread. This then
makes the algo execution of the 5000 buy order at
ask below VWAP no possible and the remainder of
the order adds to the buy side by raising the bid.
‘Advanced’ VWAP Strategies
Competition:Buy100,000 shares
over the day and max P/L
• The common complaint re. VWAP is that it
does not ‘optimize’ P/L
Crude VWAP algo does not try and get price
improvements and does not place greater volume
at more advantageous prices during the day
Eg: Consider DMGT, whilst buying using crude
VWAP one could have bought it through out
It is hard to ask your broker to do better than VWAP : but you can get
an algo to do better
Results on 1st of March, 2007
P/L
BGY
(ADV %)
DMGT
(ADV %)
Ordinary
Vwap
Advanced 1
Vwap
Advance 2
Vwap
-4612.90
-297.25
+2657.50
-1617.00
-395.00
+190.22
Advance 2 VWAP
Pairs Trading:Simple Take
• Hard to predict directional changes in stock price
movements accurately; instead take relative movements
• The pairs trade was developed in the late 1980s by
quantitative analysts and pioneered by Gerald
Bamberger while at Morgan Stanley. With the help of
others at Morgan Stanley at the time, including Nunzio
Tartaglia, Bamberger found that certain securities, often
competitors in the same sector, were correlated in their
day-to-day price movements. When the correlation broke
down, i.e. one stock traded up while the other traded
down, they would sell the outperforming stock and buy
the underperforming one, betting that the "spread"
between the two would eventually converge. (Wikipedia)
Trading Pairs
by Douglas S. Ehrman
Pairs trading is a non-directional strategy that
identifies two companies (or futures contracts)
with similar characteristics whose price
relationship is outside of its historical range. The
strategy simply buys one instrument and sells
the other in hopes that relationship moves back
toward normal. The idea is the price relationship
between two related instruments tends to
fluctuate around its average in the short term,
while remaining stable over the long term.
Pairs Trading Using price ratios and algo triggers
using (+1,-1)std deviation bands
Ingredients to Pairs Algo: At each cycle/round trip of open and close
profit non-negative
• Step 1 take a pair of stocks (BGY trading
@ 414 and DMGT @710)
• Fix quantity of cheap BGY at 1000
• Volume of DMGT is then at price ratio
414/710
• Achieves cash neutrality (zero net
position) when long in one and short in
another
How to install std deviation bands ?
Try a dynamic rolling band
• Track the price ratio for 10 minutes from opening
and take (+1, -1) std dev of it
• Project these bands to be the std dev for next 10
minutes
• Enter market as price ratio falls and hits
the -1 std deviation implying expensive stock price
has become more expensive and the cheap one
cheaper
Reverse position when price ratio hits +1 std dev
Tracking a Trade Cycle: start with buy cheap and sell the expensive
then reverse trades at +1 std and price ratio has risen
TRADE CYCLE
Trading
Cycle
Ratio Of
Prices
0.535623
0.539923
Quantity
1000
536
1000
536
Buy /Sell
BUY
SELL
SELL
BUY
Price
417.25
779
419.25
776.5
Market Time
10:25:19
10:25:08
11:52:42
11:52:42
Profit n
Loss
-417250
+417544
+ 419250
-416204
Net P/L : P/L on BGY (cheaper stock) : 2 p X1000= 2000p = £20
P/L on DMGT (expensive stock): 2.5 p x 536= 1340 = £13.40
If new price ratio is used and 539 is bought of DMGT at 776.5 then outlay is 418533.5 whic
exceeds what was sold.
Problems with crude price ratio: Use same price ratio in
first leg to determine volume of expensive whilst closing !
Consider Reversing by having to sell cheap asset and buy back the
expensive one
Price Ratio (Cheap Asset/ Expensive Asset Denominator) has gone up
These are the possibilities
BGY(Cheap)
Up
Down

DMGT (Expensive)

Up
Down
Could lose on the
expensive stock
Best outcome
Not applicable as
ratio will go down
Could lose on the
cheap stock
Problems in determining the
Std Deviation Bands
• Volatile pairs (or price ratios) give you
more trading opportunities and profits
• But how does one determine these bands
?
• If one knew the actual std deviation and
also the appropriate time window for which
to estimate the bands, there are more
profits to be had
PROFIT AND LOSS CHART
Predicted Standard Deviation
10 Minutes
LAST TRADE
PRICE RATIOS
SERIES
10540p
ASK-BID PRICE
RATIOS
-2385.5p
20 Minutes
6111.5p
Actual Standard
Deviation
10 Minutes
20 Minutes
16771.5
25578.5p
1990.5p
8110.0p
27614p
Competition: Design a simple rule that will improve on the
std dev bands for pairs trading
• Use bid ask prices rather than traded
prices to determine price ratios
• Report pairs algo profits at end of day for
the same pair of stock in the two teams
Concluding Remarks
• SETS Simulator which is precisely rebuilt from the
SETS Order Book Data will give very good test beds for
algo strategies
To be able to test out robustness of strategies
across stocks and across regimes is ‘priceless’
 CCFEA has done extensive precise price impact
analysis with sensitivity to time of day/intra day
seasonality to help in optimal trade scheduling
 Behaviour of order book in market crises eg.
August 2007 etc can be analysed in detail via the
simulator to play the E-Limit Order Book game
better
Trading Pairs
by Douglas S. Ehrman
Pairs Trading: Quantitative Methods and
Analysis
by Ganapathy Vidyamurthy - 2004 - 230 pages
Some References: Market
Microstructure
•
•
•
•
•
•
•
•
•
Hasbrouck, J., 2007, Empirical Market Microstructure: The Institutions,
Economics and Econometrics of Securities Trading
The Microstructure Approach to Exchange Rates by Richard K. Lyons
Easley, D. and M. O’Hara (1987). Price, trade size, and information in
securities markets. Journal of Financial Economics 19(1), 69–90.
Easley, D. and M. O’Hara (1992). Time and the process of security price
adjustment.
Journal of Finance 47(2), 576–606.
Eisler, Zoltan; Kertesz, Janos; Lillo, Fabrizio; Mantegna, Rosario N. 2007
Diffusive behavior and the modeling of characteristic times in limit order
executions, Quantitative Finance.
Gabaix, X., P. Gopikrishnan, V. Plerou, and H. E. Stanley (2003). A theory
of power-law distributions in financial market fluctuations. Nature 423, 267–
270.
Hasbrouck, J. (1991). Measuring the information content of stock trades.
The Journal of Finance 46(1), 179–207.
Kissell, R. and M. Glantz (2003). Optimal Trading Strategies:
Quantitative Approaches for Managing Market Impact and Trading
Risk. American Management Association.
Gode, D.K and Sunder, S.(1993). “Allocative efficiency of markets with
zero intelligence traders : markets as a partial substitute for individual
rationality”, Journal of Political Economy, 101, 119-137.
High Frequency Financial
Econometrics
•
•
•
•
•
•
•
•
Wing Lon Ng, 2007, “Analyzing Liquidity and Absorption Limits of Electronic Markets
with Volume Durations”, Mimeo
Bauwens and Hautsch (2006), Modelling Financial High Frequency Data using Point
Processes, Core Discussion Paper 2006/80., Université catholique de Louvain
Carrasco, M., and X. Chen (2002), Mixing and Moment Properties of Various GARCH
and Stochastic volatility Models, Econometric Theory, 18(1), 17—39.
Dufour, A. and Engle, R. F. (2000), The ACD-Model: Predictability of the time
between consecutive trades, Discussion paper, ISMA Centre, University of Reading.
Fernandes, M., and J. Grammig (2006), A Family of Autoregressive Conditional
Durations Models, Journal of Econometrics, 130(1), 1—23.
Härdle, W. (1990), Applied nonparametric regression, Cambridge University
Press,Cambridge.
Hautsch, N. (2002), Modelling intraday trading activity using Box-Cox ACD models,
Working paper, CoFE.
Hautsch, N. (2004), Modelling Irregularly Spaced Financial Data, Springer,
Heidelberg.
Agent Based (ACE)Approach
•
•
•
•
•
•
•
•
Reading on using ACE for real world market design:
Markose, M, et. al. A smart market for passenger road transport (SMPRT)
congestion: An application of computational mechanism design
Journal of Economic Dynamics and Control , Volume 31, Issue 6, June 2007,
Pages 2001-2032
Markose, S., J. Arifovic and S. Sunder ( 2007), Advances in Experimental and
Agent-based Modelling: Asset Markets, Economic Networks, Computational
Mechanism Design and Evolutionary Game Dynamics
Volume 31, Issue 6, pages 1801-1807
Readings for rationale for ACE in complex dynamics with contrarian structures
Arthur, W.B., (1994). “Inductive Behaviour and Bounded Rationality”, American
Economic Review, 84, pp.406-411.
Markose, S.M, 2005 , “Computability and Evolutionary Complexity : Markets as
Complex Adaptive Systems (CAS)”, Economic Journal , Vol. 115, pp.F159-F192.
Markose, S.M, 2004, “Novelty in Complex Adaptive Systems (CAS): A
Computational Theory of Actor Innovation”, Physica A: Statistical Mechanics and
Its Applications, vol. 344, pp. 41- 49. Fuller details in University of Essex,
Economics Dept. Discussion Paper No. 575, January 2004.
Markose S.M., 2006 “Gödelian Foundations of Non-Computability and
Heterogeneity In Economic Forecasting and Strategic Innovation”, Presented
at Gödel Centenary Colloquium at Computability in Europe 2006 Logical
Approaches to Computational Barriers
http://privatewww.essex.ac.uk/~scher/
Historical Data Analysis
Observed
Stock
DMGT
Market
Number of
Capitalisation Order
Daily Average Daily Average
Price
Volume
Small-Cap
43,547
804.07
4,192,801.24
BGY
Mid-Cap
46,246
448.09
6,889,446.42
GSK
Large-Cap
130,136
1,415.86 30,131,304.88
Duration Analysis
• help in analyzing length time spent in certain economic event
GSK
DGM
T
BGY
Diurnal Pattern